parquet_mcp_server

Name: parquet_mcp_server
Author: DeepSpringAI

@DeepSpringAI

Repository

About parquet_mcp_server

No overview available yet

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "parquet_mcp_server": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli",
        "install",
        "@DeepSpringAI/parquet_mcp_server",
        "--client",
        "claude"
      ]
    }
  }
}

Tools

Path to input Parquet file

Path to save the output

Column containing text to embed

Name for the new embedding column

Number of texts to process in each batch (for better performance)

Path to the Parquet file to analyze

Path to the input Parquet file

Directory to save the DuckDB database (defaults to same directory as input file)

Path to the input Parquet file

Name of the PostgreSQL table to create or append to

Path to the markdown file to process

Path to save the output parquet file

Preserves document structure and links

Overview

What is parquet_mcp_server?

A Model Context Protocol (MCP) server that provides tools for manipulating and analyzing Parquet files, designed for use with Claude Desktop. It supports text embedding generation, Parquet file analysis, conversion to DuckDB or PostgreSQL (with pgvector), and markdown file processing.

How to use parquet_mcp_server?

Install via Smithery (npx -y @smithery/cli install @DeepSpringAI/parquet_mcp_server --client claude) or clone the repository and install with uv pip install -e .. Configure environment variables in a .env file (embedding URL, Ollama URL, embedding model, PostgreSQL credentials). Add the server to Claude Desktop configuration, then invoke tools using natural language prompts or the provided client functions.

Key features of parquet_mcp_server?

Generate text embeddings from Parquet columns
Extract Parquet file schema and metadata
Convert Parquet to DuckDB databases
Convert Parquet to PostgreSQL tables with pgvector
Process markdown files into structured chunks

Use cases of parquet_mcp_server?

Analyze large Parquet datasets for data science workflows
Generate vector embeddings from text in Parquet files
Convert Parquet to DuckDB for fast in-memory querying
Convert Parquet to PostgreSQL for vector similarity search
Chunk markdown documents for retrieval‑augmented generation (RAG)

FAQ from parquet_mcp_server?

What dependencies are required to use parquet_mcp_server?

Requires Ollama server running for embedding generation, PostgreSQL with pgvector extension for PostgreSQL conversion, and Python with uv for installation.

How do I configure the server?

Set environment variables for embedding URL, Ollama URL, embedding model, and PostgreSQL credentials in a .env file.

What if embedding generation fails?

Check that the Ollama server is accessible and the specified model is available, and that the text column exists in the input Parquet file.

What if PostgreSQL conversion fails?

Verify PostgreSQL connection settings, ensure the server is running, and that the pgvector extension is installed and you have table creation

Comments

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